Deep Image Analysis Methods for Microalgae Identification

Jeffrey Soar; Oh Shu Lih; Loh Hui Wen; Aletha Ward; Ekta Sharma; Ravinesh Deo; Prabal Datta Barua; Ru-San Tan; Eliezer Rinen; U Rajendra Acharya

doi:10.20944/preprints202305.1323.v1

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preprints.org > computer science and mathematics > artificial intelligence and machine learning > doi: 10.20944/preprints202305.1323.v1

Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Deep Image Analysis Methods for Microalgae Identification

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Version 1 : Received: 17 May 2023 / Approved: 18 May 2023 / Online: 18 May 2023 (10:12:31 CEST)

How to cite: Soar, J.; Lih, O. S.; Hui Wen, L.; Ward, A.; Sharma, E.; Deo, R.; Barua, P. D.; Tan, R.-S.; Rinen, E.; Acharya, U. R. Deep Image Analysis Methods for Microalgae Identification. Preprints 2023, 2023051323. https://doi.org/10.20944/preprints202305.1323.v1 Soar, J.; Lih, O. S.; Hui Wen, L.; Ward, A.; Sharma, E.; Deo, R.; Barua, P. D.; Tan, R.-S.; Rinen, E.; Acharya, U. R. Deep Image Analysis Methods for Microalgae Identification. Preprints 2023, 2023051323. https://doi.org/10.20944/preprints202305.1323.v1

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MDPI and ACS Style

Soar, J.; Lih, O. S.; Hui Wen, L.; Ward, A.; Sharma, E.; Deo, R.; Barua, P. D.; Tan, R.-S.; Rinen, E.; Acharya, U. R. Deep Image Analysis Methods for Microalgae Identification. Preprints 2023, 2023051323. https://doi.org/10.20944/preprints202305.1323.v1

APA Style

Soar, J., Lih, O. S., Hui Wen, L., Ward, A., Sharma, E., Deo, R., Barua, P. D., Tan, R. S., Rinen, E., & Acharya, U. R. (2023). Deep Image Analysis Methods for Microalgae Identification. Preprints. https://doi.org/10.20944/preprints202305.1323.v1

Chicago/Turabian Style

Soar, J., Eliezer Rinen and U Rajendra Acharya. 2023 "Deep Image Analysis Methods for Microalgae Identification" Preprints. https://doi.org/10.20944/preprints202305.1323.v1

Abstract

Plant-based protein sources derived from microalgae offer promise as low-carbon emission and nutrient-dense sources for human consumption. The current management of microalgae cultivation relies heavily on manual microscopic examination in order to identify desired and competing species, as well as predators. In this study, we trained and tested a transfer learning model modified from EfficientNetV2 B3 on 434 and 161 prospectively acquired images of the preferred Nannochloropsis sp microalgae and competitor Spirulina, respectively, and achieved >98% classification for both species on tenfold cross-validation. The model was further enhanced with gradient-weighted class activation mapping, which allowed visualization of regions of the input images that were relevant to the classification, thereby improving its explainability. In this paper, we demonstrate that a simple deep transfer learning model can be used to identify microalgae species. The application addresses the practical need for robust automated monitoring of microalgae populations on industrial microalgae farms.

Keywords

microalgae; artificial intelligence; deep learning; sustainability

Subject

Computer Science and Mathematics, Artificial Intelligence and Machine Learning

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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